工具变量估计:假设、陷阱和指南

The Leadership Quarterly有篇论文Instrumental variables estimation: Assumptions, pitfalls, and guidelines，提供了针对工具变量研究设计、分析和报告阶段的非技术性的指南

Bastardoz, N., Matthews, M.J., Sajons, G.B., Ransom, T., Kelemen, T.K., & Matthews, S.H. (2023). Instrumental variables estimation: Assumptions, pitfalls, and guidelines. The Leadership Quarterly.

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【Abstract】Researchers striving to ensure rigor in their scientific findings face a common pitfall: Endogeneity. To tackle this problem, scholars have increasingly adopted instrumental variables estimation (IVE). Although there are many published works showing how IVE should be used, many applied researchers still have trouble understanding how to use the method correctly. In this article, we provide a methodological overview of IVE by discussing the underlying conditions valid instruments must satisfy as well as common mistakes made in using IVE. Using simulated data, we further demonstrate the sensitivity of IVE to violations of its conditions. We then take stock of the literature in a social science discipline (i.e., leadership research) and provide insights regarding trends and shortcomings in the application of IVE. Based on our review, we categorize the different types of instruments used and discuss the potential appropriateness of each type. We conclude by providing non–technical guidelines targeted at the study design, analysis, and reporting phases, which will help applied social science researchers to ensure they use IVE correctly.

【中文摘要】努力确保科学发现严谨性的研究人员面临着一个共同的陷阱:内生性。为了解决这一问题，学者们越来越多地采用工具变量估计(IVE)。尽管有许多已发表的文章展示了如何使用IVE，但许多应用研究人员仍然难以理解如何正确使用该方法。在本文中，我们通过讨论有效工具变量必须满足的基本条件以及使用IVE时常见的错误，提供了IVE的方法学概述。利用模拟数据，我们进一步证明了IVE对违反其条件的敏感性。然后，我们回顾了社会科学学科(即领导力研究)的文献，并就IVE应用中的趋势和不足提供了见解。基于我们的回顾，我们将使用的不同类型的工具变量进行分类，并讨论每种类型的潜在适当性。最后，我们提供了针对研究设计、分析和报告阶段的非技术性的指南，这将帮助社会科学研究人员确保他们正确使用IVE。

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